Text Line Segmentation in Handwritten Document Images Using Tensor Voting
Summary :
A novel grouping approach to segment text lines from handwritten documents is presented. In this text line segmentation algorithm, for each text line, a text string that connects the center points of the characters in this text line is built. The text lines are then segmented using the resulting text strings. Since the characters of the same text line are situated close together and aligned on a smooth curve, 2D tensor voting is used to reduce the conflicts when building these text strings. First, the text lines are represented by separate connected components. The center points of these connected components are then encoded by second order tensors. Finally, a voting process is applied to extract the curve saliency values and normal vectors, which are used to remove outliers and build the text strings. The experimental results obtained from the test dataset of the ICDAR 2009 Handwriting Segmentation Contest show that the proposed method generates high detection rate and recognition accuracy.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E94-A No.11 pp.2434-2441
- Publication Date
- 2011/11/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E94.A.2434
- Type of Manuscript
- PAPER
- Category
- Image
Authors
Keyword
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Toan Dinh NGUYEN, Gueesang LEE, "Text Line Segmentation in Handwritten Document Images Using Tensor Voting" in IEICE TRANSACTIONS on Fundamentals,
vol. E94-A, no. 11, pp. 2434-2441, November 2011, doi: 10.1587/transfun.E94.A.2434.
Abstract: A novel grouping approach to segment text lines from handwritten documents is presented. In this text line segmentation algorithm, for each text line, a text string that connects the center points of the characters in this text line is built. The text lines are then segmented using the resulting text strings. Since the characters of the same text line are situated close together and aligned on a smooth curve, 2D tensor voting is used to reduce the conflicts when building these text strings. First, the text lines are represented by separate connected components. The center points of these connected components are then encoded by second order tensors. Finally, a voting process is applied to extract the curve saliency values and normal vectors, which are used to remove outliers and build the text strings. The experimental results obtained from the test dataset of the ICDAR 2009 Handwriting Segmentation Contest show that the proposed method generates high detection rate and recognition accuracy.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E94.A.2434/_p
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@ARTICLE{e94-a_11_2434,
author={Toan Dinh NGUYEN, Gueesang LEE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Text Line Segmentation in Handwritten Document Images Using Tensor Voting},
year={2011},
volume={E94-A},
number={11},
pages={2434-2441},
abstract={A novel grouping approach to segment text lines from handwritten documents is presented. In this text line segmentation algorithm, for each text line, a text string that connects the center points of the characters in this text line is built. The text lines are then segmented using the resulting text strings. Since the characters of the same text line are situated close together and aligned on a smooth curve, 2D tensor voting is used to reduce the conflicts when building these text strings. First, the text lines are represented by separate connected components. The center points of these connected components are then encoded by second order tensors. Finally, a voting process is applied to extract the curve saliency values and normal vectors, which are used to remove outliers and build the text strings. The experimental results obtained from the test dataset of the ICDAR 2009 Handwriting Segmentation Contest show that the proposed method generates high detection rate and recognition accuracy.},
keywords={},
doi={10.1587/transfun.E94.A.2434},
ISSN={1745-1337},
month={November},}
Copy
TY - JOUR
TI - Text Line Segmentation in Handwritten Document Images Using Tensor Voting
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2434
EP - 2441
AU - Toan Dinh NGUYEN
AU - Gueesang LEE
PY - 2011
DO - 10.1587/transfun.E94.A.2434
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E94-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2011
AB - A novel grouping approach to segment text lines from handwritten documents is presented. In this text line segmentation algorithm, for each text line, a text string that connects the center points of the characters in this text line is built. The text lines are then segmented using the resulting text strings. Since the characters of the same text line are situated close together and aligned on a smooth curve, 2D tensor voting is used to reduce the conflicts when building these text strings. First, the text lines are represented by separate connected components. The center points of these connected components are then encoded by second order tensors. Finally, a voting process is applied to extract the curve saliency values and normal vectors, which are used to remove outliers and build the text strings. The experimental results obtained from the test dataset of the ICDAR 2009 Handwriting Segmentation Contest show that the proposed method generates high detection rate and recognition accuracy.
ER -
English
